publication . Conference object . 2017

Analysis of medical arguments from patient experiences expressed on the social web

Noor, K.; Hunter, A.; Mayer, A.;
Open Access English
  • Published: 01 Jan 2017
  • Publisher: Springer, Cham
Abstract
In this paper we present an implemented method for analysing arguments from drug reviews given by patients in medical forums on the web. For this we provide a number of classification rules which allow for the extraction of specific arguments from the drug reviews. For each review we use the extracted arguments to instantiate a Dung argument graph. We undertake an evaluation of the resulting argument graphs by applying Dung’s grounded semantics. We demonstrate a correlation between the arguments in the grounded extension of the graph and the rating provided by the user for that particular drug.
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UCL Discovery
Conference object . 2017

1. L. Amgoud and S. Vesic. Repairing preference-based argumentation systems. In Proceedings of International Joint Conference on Arti cial Intelligence, pages 665{ 670, 2009.

2. J. Cole, C. Watkins, and D. Kleine. Health advice from internet discussion forums: How bad is dangerous? Journal of Medical Internet Research, 18(1):e4, Jan 2016.

3. G. P. C.Sardianos, I. Katakis and V. Karkaletsis. Argument extraction from news. In Proceedings of the 2nd Workshop on Argumentation Mining, Association for Computational Linguistics, pages 56{66, 2015.

4. P. M. Dung. On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming, and n-person games. Articial Intelligence, 77:321{357, 1995.

5. S. Gabbriellini and F. Santini. A micro study on the evolution of arguments in amazon.com's reviews. In Proceedings of PRIMA 2015: Principles and Practice of Multi-Agent Systems, pages 284{300, 2015.

6. H. Huangbo and R. Mercer. An automated method to build a corpus of rhetoricallyclassi ed sentences in biomedical texts. In Proceedings of the First Workshop on Argumentation Mining, Association for Computational Linguistics, pages 19{23, 2014.

7. A. Hunter and M. Thimm. On partial information and contradictions in probabilistic abstract argumentation. In Proceedings of The 15th International Conference on Principles of Knowledge Representation and Reasoning, pages 53{62, 2016.

8. J. Leite and J. Martins. Social abstract argumentation. Proceedings of the TwentySecond International Joint Conference on Arti cial Intelligence, 3:2287{2292, 2011.

9. M. Lippi and P. Torroni. Argumentation mining: State of the art and emerging trends. ACM Transactions on Internet Technology, 16:1{25, 2016.

10. J. Schneider. Semi-automated argumentative analysis of online product reviews. In Proceedings of COMMA 2012: Computational Models of Arguments, pages 43{50, 2012.

11. S. Teufel. Argumentative zoning: Information extraction from scienti c text. PhD Thesis, School of Cognitive Science, University of Edinburgh, Edinburgh, UK, 1999. [OpenAIRE]

Abstract
In this paper we present an implemented method for analysing arguments from drug reviews given by patients in medical forums on the web. For this we provide a number of classification rules which allow for the extraction of specific arguments from the drug reviews. For each review we use the extracted arguments to instantiate a Dung argument graph. We undertake an evaluation of the resulting argument graphs by applying Dung’s grounded semantics. We demonstrate a correlation between the arguments in the grounded extension of the graph and the rating provided by the user for that particular drug.
Download from
UCL Discovery
Conference object . 2017

1. L. Amgoud and S. Vesic. Repairing preference-based argumentation systems. In Proceedings of International Joint Conference on Arti cial Intelligence, pages 665{ 670, 2009.

2. J. Cole, C. Watkins, and D. Kleine. Health advice from internet discussion forums: How bad is dangerous? Journal of Medical Internet Research, 18(1):e4, Jan 2016.

3. G. P. C.Sardianos, I. Katakis and V. Karkaletsis. Argument extraction from news. In Proceedings of the 2nd Workshop on Argumentation Mining, Association for Computational Linguistics, pages 56{66, 2015.

4. P. M. Dung. On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming, and n-person games. Articial Intelligence, 77:321{357, 1995.

5. S. Gabbriellini and F. Santini. A micro study on the evolution of arguments in amazon.com's reviews. In Proceedings of PRIMA 2015: Principles and Practice of Multi-Agent Systems, pages 284{300, 2015.

6. H. Huangbo and R. Mercer. An automated method to build a corpus of rhetoricallyclassi ed sentences in biomedical texts. In Proceedings of the First Workshop on Argumentation Mining, Association for Computational Linguistics, pages 19{23, 2014.

7. A. Hunter and M. Thimm. On partial information and contradictions in probabilistic abstract argumentation. In Proceedings of The 15th International Conference on Principles of Knowledge Representation and Reasoning, pages 53{62, 2016.

8. J. Leite and J. Martins. Social abstract argumentation. Proceedings of the TwentySecond International Joint Conference on Arti cial Intelligence, 3:2287{2292, 2011.

9. M. Lippi and P. Torroni. Argumentation mining: State of the art and emerging trends. ACM Transactions on Internet Technology, 16:1{25, 2016.

10. J. Schneider. Semi-automated argumentative analysis of online product reviews. In Proceedings of COMMA 2012: Computational Models of Arguments, pages 43{50, 2012.

11. S. Teufel. Argumentative zoning: Information extraction from scienti c text. PhD Thesis, School of Cognitive Science, University of Edinburgh, Edinburgh, UK, 1999. [OpenAIRE]

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